A lot of companies are trying to figure out how to use generative AI to boost their businesses. Marqeta (MQ) has recently launched Marqeta Docs AI. Marqeta CEO Simon Khalaf tells Yahoo Finance Live that there are four areas the company is using generative AI: personalization, risk reduction, better customer service, and expanding the credit equity for consumers. However Khalaf notes that in financial services, there is a high bar for using AI. "In every part of technology, generative AI has to generate content, while in financial services, most of the time, generative AI has to generate money," Khlaf said.
RACHELLE AKUFFO: This year in tech has been largely focused on generative AI. And while there are a number of issues to explore and to problem-solve, because humans are generally excited about using new platforms to streamline their activities. Now, because of this, the use of AI in fintech is expected to exceed 6.2 million by 2032. That's according to data from Market.us. Now, our next guest is one of many companies driving innovation in AI, particularly AI in the fintech space.
Marqeta is a card-issuing platform for businesses, and it's launching a new AI-powered question and answer tool called Marqeta Docs AI. Let's bring in Marqeta CEO Simon Khalaf to discuss this more. Thank you for joining me on the show this morning. So talk about the goal here and how you plan on disrupting this particular aspect of fintech for consumers.
SIMON KHALAF: Oh, Rachelle, thank you for having me and having Marqeta on your show. Actually, Yahoo Finance has a soft spot in my heart that was part of your team. And thank you for keeping the brand alive and kicking. So excited about that. So there's plenty of opportunities for AI in financial services. Actually, for us, the bar is quite high. Because let's say in every part of technology, generative AI has to generate content. While in financial services, most of the time, generative AI has to generate money.
So the bar is high. But we've added-- we're going at it. We're very excited about it. And I'd say there's four areas in which we are focused on. The first one is personalization, so that you can deliver highly-personalized services to consumers. Second one is the risk reduction. Third one is better customer service that everybody expect. And last but not least is expanding the credit equity for consumers.
RACHELLE AKUFFO: And I want to dig into that. Because one of the things we're looking at is a predictive credit card. So for the layperson who's trying to understand what that means in terms of equity and access to things like a good credit score or access to credit, what does that mean?
SIMON KHALAF: Yeah. Let me simplify it for you. I think consumers got used to personalization. If you look at the Yahoo home page, it has been personalized for about seven years. People, every person gets that own version of the Yahoo home page. The search results on Google are personalized. Your social feed is personalized. Your music list is personalized. But if you look at the credit card or the payment card, it's the most adopted piece of technology ever known to humankind. It has more distribution than Google, Yahoo, Facebook combined. And it's still a piece of plastic.
So what that means is that the card becomes a technology product. It becomes a virtual product that changes with the behavior of the consumer. So what could change? The first one is underwriting, which is how much credit do you get. But you don't get it as a person. You get it on a transaction-by-transaction basis. The second thing that would change is the rewards, which is how many rewards do you get when you purchase something. And there's many other things about a credit card that can change. It learns from your behavior as a consumer and it adapts, the same thing every other technology product has gotten.
So I think taking all the learning from media companies, from big tech platforms and applying them to financial services, and especially credit card, which is the most adopted technology product, by the way, is going to give consumers a lot, especially on the equity side.
RACHELLE AKUFFO: Because one of the things that people worry about is bias with some of these AI models. Obviously, a lot of these AI models are only as good as the data that goes into them. How do you manage potential bias that's already preexisting in the models when people are trying-- when the model is trying to figure out credit worthiness of people coming into the market?
SIMON KHALAF: Of course. I mean, there's definitely exactly as you suggested, Rachelle, models are as biased as the data that's training them. But if you look at the premise of what we are doing, we're actually breaking down the credit decision to a transaction at a time, not to a human at a time. So let me give you an example. Most of credit decisions are done based on a credit score, a FICO score, which is given to an individual. And we all know-- we all know that this score is antiquated. And I'd say that a lot of time, it is based on your historical financial behavior.
Well, what we do here at Marqeta, we actually break down a consumer to a trend-- to multiple transactions. And we actually have the ability to work with our partners and do it ourselves to underwrite you at one transaction at a time. We look at the credit risk one transaction at a time. And that significantly reduces the risk profile and at the same time open up the credit box and allow more and more equitable access to credit, one transaction at a time. So instead of, let's say, giving you, just as an example, $2,000 of credit limit, we actually focus on one transaction.
Let's say you just wanted to buy-- just fill up your car with gas and just put $100. The decision is made on that $100 specifically. And does it get paid in installment or it gets paid-- we lend you the money or our partners lend you the money. And then you've got 60 days to pay. So by breaking down the credit risk to one transaction at a time, we've changed the game here, and we've opened up the credit box for so many.
RACHELLE AKUFFO: And certainly that hyper-personalization is something that we have seen in other aspects of tech, but certainly haven't seen that as much when it comes to credit and creditworthiness. I want to talk about some of the partnerships that you have when we're looking at Block and Cash App. How do you pick the partners that you work with. And perhaps, who could be some other potential partners who you'd like to sort grow with to expand the business and grow it?
SIMON KHALAF: Rachelle, the most interesting thing is partners pick up-- pick us. We're a platform. I mean, we're aiming to be the operating system of embedded finance, which is a platform play that allows everybody who wants to embed financial services in their own application and their own workflow, to deliver to all their consumers and audiences the financial services. We're very excited about the innovation we're doing with Square and Cash App. It's actually-- it's phenomenal, the growth that this company has seen. We're excited by it.
But we have many partnerships. I can give you examples in the spaces where we have partnerships and we're seeking partnerships. I'd say marketplaces-- most importantly tech marketplaces or retail marketplaces. We can provide [INAUDIBLE] lending exactly how we talked about in terms of providing item-by-item or transaction-by-transaction lending. We can provide co-branded credit cards that interact with the marketplace, interact with the consumers, and offer rewards that are integrated with the marketplace.
At the same time, we can offer seller financing. So there's many areas in technology and in retail in which we can add a lot of value. So I'd say marketplaces would be something that we're seeking more partners to distribute and to give the consumers of these marketplaces, on both sides, buyers and sellers, great financial elasticity.
RACHELLE AKUFFO: And certainly people need as many options as possible. So certainly interesting to see the innovations that you're making in this space. Thank you so much for joining us this morning. Marqeta CEO Simon Khalaf. Thank you so much.
SIMON KHALAF: Thank you for having me Rachelle.